CN112669480B - Data processing method and device, terminal equipment and storage medium - Google Patents
Data processing method and device, terminal equipment and storage medium Download PDFInfo
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Abstract
The application is applicable to the technical field of data processing, and provides a data processing method, a device, a terminal device and a storage medium, wherein the method comprises the following steps: receiving a first terminal attribution identification carried by a first request sent by a first terminal and a second terminal attribution identification carried by a second request sent by a second terminal; when the first terminal attribution identification is the same as the second terminal attribution identification, determining a target data code from the first data code and the second data code; acquiring target characteristic data from a first database according to the first terminal attribution identification and the target data code; each feature data in the first database corresponds to a data code, and the target feature data are feature data with the data codes larger than the target data codes; and sending the target characteristic data to the first terminal and the second terminal. By adopting the method to acquire the target characteristic data from the first database, the operation workload of the server for inquiring the data from the database can be reduced.
Description
Technical Field
The present application belongs to the field of data processing technologies, and in particular, to a data processing method and apparatus, a terminal device, and a storage medium.
Background
At present, for schools using the face attendance system, each class has a corresponding face attendance system. However, when each face attendance system acquires face data from the server (updates and replaces the face data in the face attendance system), if the server responds to a request of each face attendance system, the workload of server operation is increased, and the problem of jamming and stopping of the server during operation is easily caused.
Disclosure of Invention
The embodiment of the application provides a data processing method, a data processing device, terminal equipment and a storage medium, and can solve the problem that the operation workload of a server is increased when a large number of attendance terminals simultaneously access the server to obtain face data of students.
In a first aspect, an embodiment of the present application provides a data processing method, including:
receiving a first request sent by a first terminal and a second request sent by a second terminal;
judging whether a first terminal attribution identification carried by the first request is the same as a second terminal attribution identification carried by the second request;
if the first terminal attribution identification is the same as the second terminal attribution identification, determining a smaller data code as a target data code from a first data code carried by the first request and a second data code carried by the second request;
acquiring target characteristic data from a first database according to the first terminal attribution identification and the target data code; the first database stores a plurality of characteristic data corresponding to the first terminal attribution identification, each characteristic data corresponds to one data code, and the larger the data code is, the later the time for inputting the corresponding characteristic data into the first database is; the target characteristic data is characteristic data with a data code larger than the target data code;
and sending the target characteristic data to the first terminal and the second terminal.
In an embodiment, before the receiving the first request sent by the first terminal and the second request sent by the second terminal, the method further includes:
respectively distributing a data code for each characteristic data;
and sequentially storing each characteristic data into the storage positions in the first database according to the size sequence of the data codes.
In an embodiment, the method further comprises:
according to the size sequence of the data codes and the quantity of the plurality of characteristic data, sequentially writing a preset quantity of characteristic data into one text file to obtain a plurality of text files of the plurality of characteristic data;
compressing the text files into data packets, and uploading the data packets to a cloud storage server;
when a data updating request of a newly added terminal is received, determining storage position information of the data packet in the cloud storage server;
and sending the storage position information to the newly added terminal so that the newly added terminal can acquire the data packet from the cloud storage server according to the storage position information.
In an embodiment, the first database stores a plurality of first feature data corresponding to a first terminal attribution identifier and a plurality of second feature data corresponding to a second terminal attribution identifier, and the method further includes;
if the first terminal attribution identification is different from the second terminal attribution identification, determining a plurality of first feature data corresponding to the first terminal attribution identification in the first database, extracting first target feature data with a data code larger than the first data code from the plurality of first feature data, and sending the first target feature data to the first terminal; and the number of the first and second groups,
if the first terminal attribution identification is different from the second terminal attribution identification, determining a plurality of second feature data corresponding to the second terminal attribution identification in the first database, extracting second target feature data with a data code larger than the second data code from the plurality of second feature data, and sending the second target feature data to the second terminal.
In one embodiment, the feature data is face feature data used for data processing; after the sending the target characteristic data to the first terminal and the second terminal, the method further comprises:
obtaining attendance checking results respectively uploaded by the first terminal and the second terminal; the attendance checking result is generated by comparing the face feature data to be checked with the face feature data stored in the first terminal or the second terminal respectively by the first terminal and the second terminal;
and storing the attendance checking result to a second database corresponding to the first terminal attribution identification.
In an embodiment, before the storing the attendance result to the second database corresponding to the first terminal attribution identifier, the method further includes:
establishing a second database according to the first terminal attribution identification, wherein the second database is used for storing an attendance result corresponding to the first terminal attribution identification;
establishing a binding relation between the data address of the second database and the home identifier of the first terminal;
and storing the binding relationship in a table form.
In an embodiment, the storing the binding relationship in a table form includes:
taking the attribution identification of the first terminal as a key value, and taking the data address of the second database as a value to form a key value pair;
storing the key-value pairs into a key-value database so that the binding relationships are stored in the key-value database in a tabular form.
In a third aspect, an embodiment of the present application provides a terminal device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor, when executing the computer program, implements the method according to any one of the above first aspects.
In a fourth aspect, the present application provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the computer program implements the method according to any one of the above first aspects.
In a fifth aspect, the present application provides a computer program product, which when run on a terminal device, causes the terminal device to execute the method of any one of the above first aspects.
In this embodiment of the application, in this embodiment, by obtaining a first terminal attribution identifier and a first data code of a first terminal, and a second terminal attribution identifier and a data code of a second terminal, when it is determined that the first terminal attribution identifier is the same as the second terminal attribution identifier, a storage location is determined from a first database according to the first terminal attribution identifier or the second terminal attribution identifier, and face feature data of a plurality of students in a first school in the storage location are determined. And then, determining a target data code from the first data code and the second data code, so that the terminal equipment only needs to inquire a plurality of target characteristic data once from the face characteristic data according to one target data code. Therefore, the terminal equipment does not need to inquire the face characteristic data according to the data codes in the request of each attendance terminal, the inquiry times of the terminal equipment are reduced, and the workload of server operation is reduced.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a flowchart illustrating an implementation of a data processing method according to an embodiment of the present application;
fig. 2 is a flowchart of an implementation of a data processing method according to another embodiment of the present application;
FIG. 3 is a flowchart illustrating an implementation of a data processing method according to another embodiment of the present application;
FIG. 4 is a flowchart illustrating an implementation of a data processing method according to yet another embodiment of the present application;
FIG. 5 is a flowchart illustrating an implementation of a data processing method according to yet another embodiment of the present application;
FIG. 6 is a flowchart illustrating an implementation of a data processing method according to yet another embodiment of the present application;
fig. 7 is a schematic diagram illustrating a storage relationship between a terminal home identifier and a second database address in a data processing method according to an embodiment of the present application;
fig. 8 is a schematic view of an application scenario of a data processing method according to an embodiment of the present application;
fig. 9 is a block diagram of a data processing apparatus according to an embodiment of the present application;
fig. 10 is a block diagram of a terminal device according to an embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. However, it will be apparent to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
It should also be understood that the term "and/or" as used in this specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items and includes such combinations.
Furthermore, in the description of the present application and the appended claims, the terms "first," "second," "third," and the like are used for distinguishing between descriptions and not necessarily for describing a relative importance or importance.
The data processing method provided by the embodiment of the application can be applied to terminal devices such as a mobile phone, a tablet computer, a notebook computer, a super-mobile personal computer (UMPC), a netbook and the like, and can also be applied to terminal devices such as an intelligent banquet tablet, an attendance terminal and a server, and the embodiment of the application does not limit the specific types of the terminal devices.
In the prior art, for a face attendance system used in each class, when a plurality of face attendance terminals simultaneously acquire face data from a server, if the server responds to a request of each face attendance terminal, the workload of server operation is increased, and the problem of server jamming during operation is easily caused. The face characteristic data does not need to be inquired according to the request of each face attendance terminal when the server receives the requests of a plurality of face attendance terminals. Based on the face characteristic data, when a plurality of face attendance terminals in the same school respectively send requests, the face characteristic data can be inquired only according to the request of one face attendance terminal, and the inquiry times of the server are reduced.
Referring to fig. 1, fig. 1 is a flowchart illustrating an implementation of a data processing method according to an embodiment of the present application, where the method includes the following steps:
s101, receiving a first request sent by a first terminal and a second request sent by a second terminal.
In application, the first terminal may be an attendance terminal of a school, and is used for checking attendance of students. Specifically, the attendance terminal can extract facial features according to the facial information of discernment student, compares one by one with the facial feature data of attendance terminal internal storage, and then carries out the attendance to the student. The first request is a request for the first terminal to acquire attendance data from the server. The second terminal and the first terminal can be different attendance terminals of the same school, and can also be attendance terminals of different schools respectively.
S102, judging whether the first terminal attribution identification carried by the first request is the same as the second terminal attribution identification carried by the second request.
In application, the first terminal attribution identification can be a number, a letter, or a code of a combination of the number and the letter. The terminal has unique identification and is used for identifying the school to which the first terminal belongs specifically. It is to be understood that the first terminal attribution identity may also be a school ID of the first school. It will be appreciated that the second terminal home identity is in a format consistent with the first terminal home identity. In addition, the terminal attribution identifier may be a terminal attribution identifier generated by the server when the user uses the first terminal to register a school on the server.
S103, if the first terminal attribution identification is the same as the second terminal attribution identification, determining a smaller data code as a target data code from a first data code carried by the first request and a second data code carried by the second request.
In application, when the first terminal attribution identification is judged to be the same as the second terminal attribution identification, the first terminal and the second terminal can be considered to belong to the attendance terminal of the same school. The first data code may be a number, a letter, or a combination of the number and the letter, and the second data code is in a format consistent with the first data code, which will not be described in detail. For example, if the data is encoded as numbers, the target data may be encoded with respect to the number size. If the data codes are letters, letters with the prior sequencing can be used as target data codes according to the sequence of the letters.
In application, for requests of a plurality of attendance terminals of the same school, the server can determine a target data code from data codes in the requests. And then, the server can query the face characteristic data only according to the target data codes, and does not need to query the face characteristic data according to the data codes in each attendance terminal request, so that the query times of the server are reduced.
S104, acquiring target characteristic data from a first database according to the first terminal attribution identification and the target data code; the first database stores a plurality of characteristic data corresponding to the first terminal attribution identification, each characteristic data corresponds to one data code, and the larger the data code is, the later the time for inputting the corresponding characteristic data into the first database is; the target characteristic data is characteristic data with data codes larger than the target data codes.
In application, the first database may be considered as a master database inside the server. The master database is mainly used for storing public data and basic data of student information. For example, student information includes, but is not limited to, grade, class, facial feature data, name, and the like. For student information of all students of one school, the student information may be stored in a storage area designated in the master database. And then, associating the storage area with the terminal attribution identification to form a mapping relation between the terminal attribution identification and the storage area. That is, it can be considered that the storage area (storage location) corresponding to the facial feature data of the school in the first database can be determined according to the terminal attribution identifier.
In application, when the face feature data of each student is written into the first database, a data code is generated for each face feature data, and the data codes are used for distinguishing the face feature data of a plurality of students. The larger the data code is, the later the time for inputting the corresponding face feature data into the first database is. It is understood that the data corresponding to the face feature data are encoded into numbers sequentially ordered from 1 to N (N > 1). Namely, the data codes are determined according to the sequence of the input time of the face feature data of each student.
It should be added that the method for generating the data code may be generated by using a Universal Unique Identifier (UUID) method. Specifically, UUID is a standard for software construction, and is also part of the field of open software distributed computing environments. The purpose is to make all elements in the distributed system have unique identification information without additional specification of the identification information by a processor. When the server generates the data code by using the UUID, the data code of each face feature data can be given according to the numerical sequence from 1 to N (N > 1).
In one embodiment, there are a plurality of students, such as 40 students, for the first school. If a new student is added to the first school at the current time, the face feature data and the new data code of the new student are added to the first database in the server (for example, 41). The attendance checking terminal determines that the maximum value of data codes in the stored data codes corresponding to a plurality of face feature data is 40 according to the face feature data of all students (40 students) downloaded from the first database at the previous moment and the data codes (1-40 data codes) of each face feature data. Thereafter, a data encoding maximum (40) may be sent to the server as a first data encoding in the first request. Under normal conditions, the server can take the first data code as a target data code (40), and send the face feature data (face feature data of a newly added student at the current moment) corresponding to the data code (41) after the target data code as the target feature data. However, if the second terminal and the first terminal are different terminals of the same school, and the second data code (for example, 30) in the second request sent by the second terminal is smaller than the first data code (40), it is determined that the second data code is the target data code (30). Then, the human face feature data corresponding to the data codes (31-41) after the target data codes are sent as target feature data. At this time, after receiving the target feature data between 31 and 40, the first terminal may delete or update the stored target feature data (the face feature data of 31 to 40). Based on the method, when a plurality of requests of the same school are acquired, the server can inquire the face characteristic data only according to the target data codes, but does not inquire the face characteristic data according to the request of each attendance terminal, and the inquiry frequency of the server is reduced.
And S105, sending the target characteristic data to the first terminal and the second terminal.
In application, the server sends the target feature data to the first terminal and the second terminal, and specifically, the target feature data can be sent to the feature value library of the first terminal and the feature value library of the second terminal respectively. The characteristic value library is a database in which the attendance checking terminals store face characteristic data of students, and when the first terminal checks attendance of the students, the first terminal can compare the face characteristic data of the students with the face characteristic data in the characteristic value library one by one to generate an attendance checking result. It can be understood that, at this time, the face feature data stored in the feature value library includes the new face feature data after the target data is encoded, the face feature data corresponding to the target data that has been stored in advance, and the face feature data corresponding to the target data before the target data is encoded.
In this embodiment, by obtaining a first terminal attribution identifier and a first data code of a first terminal, and a second terminal attribution identifier and a data code of a second terminal, when it is determined that the first terminal attribution identifier is the same as the second terminal attribution identifier, a storage location and face feature data of a plurality of students in a first school at the storage location are determined from a first database according to the first terminal attribution identifier or the second terminal attribution identifier. And then, determining a target data code from the first data code and the second data code, so that the terminal equipment only needs to query a plurality of target characteristic data once from the face characteristic data according to one target data code. Therefore, the terminal equipment does not need to inquire the face characteristic data according to the data codes in the request of each attendance checking terminal, the inquiry times of the terminal equipment are reduced, and the operation workload of the server is reduced.
Referring to fig. 2, in an embodiment, before receiving the first request sent by the first terminal and the second request sent by the second terminal in S101, the following steps S201-S202 are further included, which are detailed as follows:
s201, respectively distributing a data code for each characteristic data when the characteristic data are recorded.
S202, sequentially storing each feature data into a storage position in the first database according to the size sequence of the data codes.
In application, the above-mentioned assigning a data code to each feature data may refer to the explanation in S104, which will not be discussed. When the first terminal uses the data processing method, the first terminal needs to register the school information of the first school on the server and store the registration information in the server. The registration information includes a first terminal attribution identification of the first school and student information data. The first terminal attribution identifier may be an attribution identifier of the school, which is generated by the server itself when the first terminal registers. The student information data includes, but is not limited to, information data such as grade, class, face feature data, name, and the like. It is to be understood that, for the purpose of registering a school in the server, the registration information may be uploaded by the first terminal, and may also be uploaded by other attendance terminals associated with the first school, which is not limited herein.
In use, each feature data is stored in a storage location in a first database. Specifically, for any acquired registration information of a school, the storage location for storing the student information data of the school needs to be separately divided in the first database, so as to distinguish the student information data of each school. The server can establish an association relation between the terminal attribution identification and the storage position, so that after the server acquires the terminal attribution identification carried by the request, the server can determine the storage position of the student information data of the corresponding school in the first database according to the terminal attribution identification.
Referring to fig. 3, in an embodiment, the data processing method further includes the following steps S301 to S304, which are detailed as follows:
s301, according to the size sequence of the data codes and the number of the plurality of feature data, writing a preset number of feature data into one text file in sequence, so as to obtain a plurality of text files of the plurality of feature data.
In application, the size sequence of the data codes can be understood as a small-to-small sequence, namely a time sequence of inputting the face feature data into the first database. The number of the plurality of feature data is the number of the school students. The preset number is the number preset in the server by the user and can be set according to actual conditions. It will be appreciated that the amount of feature data is typically very large, and the maximum amount that can be covered by a text file is typically much smaller than the amount of feature data. Therefore, the preset number may be considered to be smaller than the number of the plurality of feature data. Based on this, when the preset number is smaller than the number of the plurality of feature data, the preset number of feature data is written into one text file in sequence, and a plurality of text files can be obtained. If the preset number is larger than or equal to the number of the plurality of feature data, only one text file needs to be written with the feature data.
In the application, the first face feature data of each student has a feature value of 1kb to 2kb characters therein. Specifically, an example of the face feature data is as follows:
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＝＝。
s302, compressing the text files into data packets, and uploading the data packets to a cloud storage server.
In application, after the plurality of text files are obtained, the plurality of text files can be compressed to obtain a data packet, and the data packet is stored in the cloud storage server. The cloud storage server may be an integrated service operation and management platform (OSS) server, and is configured to manage the uploaded data packet. It should be noted that, the compressed text files are uploaded to the cloud storage server, so that when a user performs a registries correction in the server, the server can directly write the facial feature data into the text files after storing the uploaded facial feature data into the first database. And then, compressing the plurality of text files, and storing the compressed text files into a cloud storage server. At the moment, the face feature data of all students in the school are stored in the cloud storage server. Meanwhile, the cloud storage server can establish an association relationship between the storage position information of all the student face feature data of the school and the terminal attribution identification according to the terminal attribution identification, so that the cloud storage server can determine the face feature data required to be issued according to the terminal attribution identification.
And S303, when a data updating request of a newly added terminal is received, determining the storage position information of the data packet in the cloud storage server.
S304, the storage position information is sent to the newly added terminal, so that the newly added terminal can obtain the data packet from the cloud storage server according to the storage position information.
In application, the newly added terminal can be understood as an attendance terminal newly added in the school, and the characteristic value library of the terminal does not store the face characteristic data of any student. Therefore, the attendance checking terminal needs to request all the face feature data of the students from the server. Based on this, the request uploaded by the attendance checking terminal is the data updating request. After receiving the data updating request, the server can send the data updating request and the terminal attribution identification to the cloud storage server, so that the cloud storage server can determine the storage location information of the data packet.
In application, the cloud storage server can only send the storage location information to the newly added terminals of the school, and then the newly added terminals download corresponding data packets from the cloud storage server according to the storage location information. The problem that when the data volume of all student face features is large when the newly added terminal obtains the data volume of all student face features, the newly added terminal directly requests all school face feature data through a network is solved, data transmission becomes slow, and large data transmission pressure can be caused on a cloud storage server and the newly added terminal.
It should be added that the cloud storage server may belong to a different main device from the server including the first database, so that when the server including the first database determines and transmits the target feature data according to the target data code, the cloud storage server does not interfere with the instruction for transmitting all student face feature data executed by the cloud storage server according to the data update request. In addition, when any server breaks down and needs to be restarted, other servers can also work normally. And the face feature data of the students are stored in different servers in different forms, so that corresponding data can be obtained in other servers when any server loses data due to system breakdown, and the safety of storing the face feature data is ensured. The cloud storage server may belong to the same main device as the server including the first database, that is, the server including the first database may further have a service function of storing a data packet. The functional component facility where the service function is located is the cloud storage server. At the moment, the face feature data of all students are compressed into a data packet to be sent, and the data transmission pressure of the server containing the first database is reduced.
Referring to fig. 4, in an embodiment, the data processing method further includes the following steps S401 to S402, which are detailed as follows:
s401, if the first terminal attribution identification is different from the second terminal attribution identification, determining a plurality of first feature data corresponding to the first terminal attribution identification in the first database, extracting first target feature data with a data code larger than the first data code from the plurality of first feature data, and sending the first target feature data to the first terminal; and the number of the first and second groups,
s402, if the first terminal attribution identification is different from the second terminal attribution identification, determining a plurality of second feature data corresponding to the second terminal attribution identification in the first database, extracting second target feature data with a data code larger than the second data code from the plurality of second feature data, and sending the second target feature data to the second terminal.
In application, the first database stores face feature data of each school. When the first terminal attribution identification is different from the second terminal attribution identification, the first school and the second school can be determined to be two schools. Therefore, when the first data code corresponding to the uploaded first terminal is the unique data code uploaded by the first school, the first data code can be determined as the target data code corresponding to the first terminal. Similarly, when the second data code corresponding to the uploaded second terminal is the unique data code uploaded by the second school, the second data code may be determined as the target data code corresponding to the second terminal. If there are requests uploaded by other terminals at the same time, the terminal attribution identifier carried in the request uploaded by the terminal needs to be compared with the first terminal attribution identifier and the second terminal attribution identifier respectively. And if the terminal attribution identification in the requests uploaded by the other terminals is consistent with the first terminal attribution identification or the second terminal attribution identification, determining that the schools corresponding to the other terminals in the first school are the same school, or the schools corresponding to the other terminals in the second school are the same school.
It will be appreciated that the steps of the server processing a first request uploaded by the first terminal after determining that the first school and the second school belong to different schools are similar to the steps of the server processing a second request uploaded by the second terminal. The method can be considered as determining the storage positions of the corresponding face feature data in the first database according to the terminal attribution identification in the respective request, and acquiring the target feature data of the corresponding school student from the storage positions according to the target data code corresponding to each terminal. And then, respectively transmitting the target characteristic data to the first terminal or the second terminal.
Referring to fig. 5, in an embodiment, in the data processing method, the feature data is face feature data used for attendance checking of students; after sending the target feature data to the first terminal and the second terminal in S105, the following steps S105A to S105B are further included, which are detailed as follows:
S105A, obtaining attendance checking results uploaded by the first terminal and the second terminal respectively; and the attendance checking result is generated by comparing the face characteristic data to be checked with the face characteristic data stored in the first terminal or the second terminal respectively by the first terminal and the second terminal.
And S105B, storing the attendance checking result to a second database corresponding to the first terminal attribution identification.
In application, the attendance result comprises a first attendance result uploaded by the first terminal and a second attendance result uploaded by the second terminal. The first terminal compares the face feature data to be checked with the face feature data stored in the feature value library of the first terminal to generate a first checking-in result. The method for generating the second attendance result by the second terminal is consistent with the method for generating the first attendance result by the first terminal, and the method is not described.
In application, the second database is not consistent with the first database, the first database is already explained to be used for storing student information, and the second database is only used for storing attendance checking results. When the user registers the school on the server, the association relation between the terminal attribution identification of the school and the second database can be established. In particular, the server may automatically build a second database for a school whenever the user registers for the school on the server. Specifically, when the user performs school registration, the server may execute a Structured Query Language (SQL) that creates the second database. For example, "create database … …" to create a second database for the school.
In application, the schools all over the country are large in number, the attendance checking result and the face feature data are stored separately in a storage mode of a master-slave database (a first database and a second database), and storage pressure of the first database for storing student information of all schools can be reduced.
Referring to fig. 6, in an embodiment, before S105B stores the attendance result in the second database corresponding to the first terminal home identifier, the following steps S105C to S105D are further included, which are detailed as follows:
and S105C, establishing a second database according to the first terminal attribution identification, wherein the second database is used for storing the attendance checking result corresponding to the first terminal attribution identification.
S105D, establishing a binding relationship between the data address of the second database and the attribution identification of the first terminal.
And S105E, storing the binding relationship in a table form.
In a specific embodiment, the step S105E of storing the binding relationship in a table further includes the following sub-steps, which are detailed as follows:
taking the attribution identification of the first terminal as a key value, and taking the data address of the second database as a value to form a key value pair;
storing the key-value pair to a key-value database, so that the binding relationship is stored in the key-value database in a table form.
In application, the above-mentioned manner of establishing the second database according to the first terminal attribution identifier has been explained in the above-mentioned S105B, and specifically, the above-mentioned contents may be referred to. The binding relationship is a one-to-one mapping relationship between the data address of the second database and the home identifier of the first terminal.
In the application, the server further comprises a key-value database for storing the binding relationship. The storing of the binding relationship in the form of a table may be storing the binding relationship in a key-value database in the server, where the database is a database storing data with key value pairs. Specifically, the key is a terminal attribution identifier, the value is a second database address, specifically, reference may be made to fig. 7, and fig. 7 is a table formed by the corresponding binding relationship between the key and the value. The key-value database has the characteristics of high query speed, large data storage amount and high support for concurrency. Compared with other types of databases, the key-value database is very suitable for storing the terminal attribution identification and the second database address with single-to-single association relationship, and therefore, the server can conveniently inquire target data (the second database address) through a primary key (key value) quickly.
In other embodiments, after sending the target feature data to the first terminal and the second terminal in S105, the method further includes the following steps:
and if the first request of the first terminal and/or the second request of the second terminal are not received again within a preset time period, disconnecting the data connection between the first database and the first terminal and/or the second terminal. And/or the presence of a gas in the gas,
when the current time is a preset time, if it is determined that the first request of the first terminal and/or the second request of the second terminal are not acquired at the current time, disconnecting the data connection between the first terminal and/or the second terminal and the first database when the connection exists between the first terminal and/or the second terminal and the first database.
In application, the preset time period may be a time period set by a user according to an actual situation, and specifically, the preset time period may be 10S. It should be noted that the connection mode between the first terminal and the second terminal and the first database in the terminal device is long connection. The long connection means that a plurality of packets can be continuously transmitted over one connection, and if no packet is transmitted during the connection holding period, both (the server and the attendance terminal) can maintain the long connection by transmitting a link detection packet. However, the first database stores face feature data of a plurality of school students, and if long connections are maintained with a plurality of attendance terminals at the same time, the number of connections of the first database is occupied. Therefore, after the method for querying the target characteristic data of each school is executed according to the target data codes, if the request of the attendance terminal is not acquired again within the preset time period, the data connection between the first database and the attendance terminal is disconnected, and the number of connections between the first database and the attendance terminal is reduced.
In application, the preset time may also be a time point set by a user according to an actual situation, specifically, the preset time may be 3:00. it should be noted that, for a server executing the data processing method, a vulnerability (bug) may be generated in the server during execution, so that an attendance terminal that should be disconnected from the first database fails to be disconnected from the long connection. Therefore, to prevent a situation where an abnormal situation causes the connection between the first database and the attendance terminal to be not disconnected, it may be set that 3:00 determining whether the request of the attendance terminal is acquired at the current moment. If the fact that the request is not acquired is judged, and long connection exists between the attendance terminal and the first database, the long connection between the attendance terminal and the first database is disconnected, so that the number of connections between the first database and the attendance terminal is reduced.
In application, the above steps and methods may also be applied to a case where there is a connection between the attendance terminal and the second database, and a manner of please disconnect the data connection is consistent with a manner of disconnecting the attendance terminal and the first database, which will not be described again.
In other embodiments, please refer to fig. 8, and fig. 8 shows an application scenario diagram of a data processing method provided in the embodiment of the present application, which is detailed as follows:
the first terminal comprises a class board 1, a teacher terminal 2 and a student terminal 3, and the terminal equipment is a server. Wherein the class card 1 can be used to upload registration information (the first terminal affiliation identifier of the first school and the student information data) to the server.
In application, the servers include an ari cloud server 4, a communication server 5, a data center server 6 and an attendance cloud server 7. The aricloud server 4 includes a first database 41 and a second database 42, and the aricloud server 4 may divide storage locations for storing student information data according to the first terminal attribution identifier in the internal first database 41 (master database), and store the student information data in the storage locations in the first database 41. Then, a second database 42 (slave database) is established according to the attribution identification of the first terminal, and is used for storing the attendance checking result uploaded by the first terminal. The teacher terminal 2 and the student terminals 3 can be used as attendance terminals for teachers and students, respectively. The communication connection between the first terminal and the ali cloud server 4 can be connected through the attendance cloud server 7, the communication server 5 and the data center server 6. The communication server 5 may use Internet of Things 51 (IoT) and/or wireless access point 52 (AP) for data transmission, which is not limited herein. After the first terminal uploads the registration information to the attendance cloud server 7, the attendance cloud server 7 can extract features of face images of students in the registration information to obtain first face feature data, and uploads the first face feature data to the ari cloud server 4.
In application, the above-mentioned arri cloud server 4 further includes a cluster service 43 and a cloud storage service 44, where the cluster service 43 may upload the attendance result through a Message Queue Telemetry Transport (MQTT) protocol, or may transmit data by using an MQTT protocol when data needs to be sent. Among them, power consumption using the MQTT Protocol is low relative to power consumption using other protocols, for example, lower than power consumption using a hypertext Transfer Protocol (HTTP) Protocol. In addition, for both the first request uploaded by the first terminal and the second request uploaded by the second terminal, the requests are first transmitted to the cluster service 43. The trunking service 43 then determines whether the first terminal home identifier is the same as the second terminal home identifier, and determines the target data code from the first data code and the second data code when the first terminal home identifier is the same as the second terminal home identifier. Then, the target data code and the attribution identification of any terminal are transmitted to the data center server 6 through the communication server 5, and the data reading service in the data center server 6 acquires the target characteristic data from the first database. The cloud storage service 44 (which may be regarded as the cloud storage server in S304) may also be configured to send the student face feature data to the first terminal. Specifically, because the data size of the facial feature data is very large, when the first terminal directly requests all the facial feature data of the first school, the data transmission will be very slow, and a large transmission pressure will be caused to the server and the first terminal. Therefore, the attendance cloud server 7 can write the extracted face feature data of the students into a plurality of text files. The plurality of text files are then compressed into a data package, and the compressed data package is uploaded to the cloud storage service 44. Finally, the cloud storage service 44 directly returns the download address of the data packet to the first terminal according to the first request.
Referring to fig. 9, fig. 9 is a block diagram of a data processing apparatus according to an embodiment of the present disclosure. The data processing apparatus in this embodiment includes modules for executing the steps in the embodiments corresponding to fig. 1 to fig. 6. Please specifically refer to fig. 1 to 6 and related descriptions of embodiments corresponding to fig. 1 to 6. For convenience of explanation, only the portions related to the present embodiment are shown. Referring to fig. 9, the data processing apparatus 900 includes: a receiving module 910, a determining module 920, a first determining module 930, a first obtaining module 940 and a first sending module 950, wherein:
a receiving module 910, configured to receive a first request sent by a first terminal and a second request sent by a second terminal.
A determining module 920, configured to determine whether the first terminal attribution identifier carried in the first request is the same as the second terminal attribution identifier carried in the second request.
A first determining module 930, configured to determine, if the first terminal attribution identifier is the same as the second terminal attribution identifier, a smaller data code as a target data code from a first data code carried in the first request and a second data code carried in the second request.
A first obtaining module 940, configured to obtain target feature data from a first database according to the first terminal attribution identifier and the target data code; the first database stores a plurality of characteristic data corresponding to the first terminal attribution identification, each characteristic data corresponds to one data code, and the larger the data code is, the later the time for inputting the corresponding characteristic data into the first database is; the target characteristic data is characteristic data with a data code larger than the target data code.
A first sending module 950, configured to send the target feature data to the first terminal and the second terminal.
In an embodiment, the data processing apparatus 900 further comprises:
and the distribution module is used for distributing a data code to each characteristic data.
And the first storage module is used for sequentially storing each characteristic data to the storage positions in the first database according to the size sequence of the data codes.
In an embodiment, the data processing apparatus 900 further comprises:
and the writing module is used for sequentially writing a preset amount of characteristic data into one text file according to the size sequence of the data codes and the amount of the characteristic data to obtain a plurality of text files of the characteristic data.
And the compression module is used for compressing the text files into data packets and uploading the data packets to the cloud storage server.
And the second determining module is used for determining the storage position information of the data packet in the cloud storage server when a data updating request of a newly-added terminal is received.
And the second sending module is used for sending the storage position information to the newly added terminal so that the newly added terminal can obtain the data packet from the cloud storage server according to the storage position information.
In an embodiment, the first database stores a plurality of first feature data corresponding to the first terminal attribution identifier and a plurality of second feature data corresponding to the second terminal attribution identifier, and the data processing apparatus 900 further includes:
a third determining module, configured to determine, if the first terminal attribution identifier is different from the second terminal attribution identifier, multiple first feature data corresponding to the first terminal attribution identifier in the first database, extract, from the multiple first feature data, first target feature data with a data code larger than the first data code, and send the first target feature data to the first terminal. And the number of the first and second groups,
a fourth determining module, configured to determine, if the first terminal attribution identifier is different from the second terminal attribution identifier, multiple second feature data corresponding to the second terminal attribution identifier in the first database, extract, from the multiple second feature data, second target feature data with a data code larger than the second data code, and send the second target feature data to the second terminal.
In one embodiment, the feature data is face feature data used for data processing; the data processing apparatus 900 further comprises:
the second acquisition module is used for acquiring attendance checking results uploaded by the first terminal and the second terminal respectively; and the attendance checking result is generated by comparing the face characteristic data to be checked with the face characteristic data stored in the first terminal or the second terminal respectively by the first terminal and the second terminal.
And the second storage module is used for storing the attendance checking result to a second database corresponding to the first terminal attribution identification.
In an embodiment, the data processing apparatus 900 further comprises:
the first establishing module is used for establishing a second database according to the first terminal attribution identification, and the second database is used for storing the attendance checking result corresponding to the first terminal attribution identification.
And the second establishing module is used for establishing the binding relationship between the data address of the second database and the home identifier of the first terminal.
And the third storage module is used for storing the binding relationship in a table form.
In one embodiment, the server further comprises a key-value database; the third storage module is further configured to:
taking the attribution identification of the first terminal as a key value, and taking the data address of the second database as a value to form a key value pair;
storing the key-value pairs into the key-value database such that the binding relationships are stored in the key-value database in a tabular form.
It should be understood that, in the structural block diagram of the data processing apparatus shown in fig. 9, each unit/module is used to execute each step in the embodiment corresponding to fig. 1 to 6, and each step in the embodiment corresponding to fig. 1 to 6 has been explained in detail in the above embodiment, specifically please refer to the relevant description in the embodiments corresponding to fig. 1 to 6 and fig. 1 to 6, which is not repeated herein.
Fig. 10 is a block diagram of a terminal device according to another embodiment of the present application. As shown in fig. 10, the terminal device 1000 of this embodiment includes: a processor 1010, a memory 1020, and a computer program 1030, such as a program of a data processing method, stored in the memory 1020 and executable on the processor 1010. The processor 1010, when executing the computer program 1030, implements the steps in the various embodiments of the data processing methods described above, such as S101 to S105 shown in fig. 1. Alternatively, when the processor 1010 executes the computer program 1030, the functions of the units in the embodiment corresponding to fig. 9, for example, the functions of the modules 910 to 950 shown in fig. 9, please refer to the related description in the embodiment corresponding to fig. 9.
Illustratively, the computer program 1030 may be divided into one or more units, which are stored in the memory 1020 and executed by the processor 1010 to accomplish the present application. One or more of the elements may be a series of computer program instruction segments capable of performing certain functions that are used to describe the execution of the computer program 1030 in the terminal device 1000.
The terminal equipment may include, but is not limited to, a processor 1010, a memory 1020. Those skilled in the art will appreciate that fig. 10 is only an example of the terminal device 1000, and does not constitute a limitation to the terminal device 1000, and may include more or less components than those shown, or some of the components may be combined, or different components, e.g., the terminal device may further include an input-output device, a network access device, a bus, etc.
The processor 1010 may be a central processing unit, or may be other general-purpose processor, a digital signal processor, an application specific integrated circuit, an off-the-shelf programmable gate array or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The storage 1020 may be an internal storage unit of the terminal device 1000, such as a hard disk or a memory of the terminal device 1000. The memory 1020 may also be an external storage device of the terminal device 1000, such as a plug-in hard disk, a smart memory card, a flash memory card, etc. provided on the terminal device 1000. Further, the memory 1020 may also include both internal and external memory units of the terminal device 1000.
The above embodiments are only used to illustrate the technical solutions of the present application, and not to limit the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.
Claims (10)
1. A method of data processing, the method comprising:
receiving a first request sent by a first terminal and a second request sent by a second terminal; the first terminal and the second terminal are different attendance terminals;
judging whether a first terminal attribution identification carried by the first request is the same as a second terminal attribution identification carried by the second request;
if the first terminal attribution identification is the same as the second terminal attribution identification, determining a smaller data code as a target data code from a first data code carried by the first request and a second data code carried by the second request;
acquiring target characteristic data from a first database according to the first terminal attribution identification and the target data code; the first database stores a plurality of characteristic data corresponding to the first terminal attribution identification, each characteristic data corresponds to one data code, and the larger the data code is, the later the time for inputting the corresponding characteristic data into the first database is; the target characteristic data is characteristic data with data codes larger than the target data codes;
and sending the target characteristic data to the first terminal and the second terminal.
2. The data processing method of claim 1, wherein before the receiving the first request sent by the first terminal and the second request sent by the second terminal, further comprising:
respectively allocating a data code to each characteristic data;
and sequentially storing each characteristic data into the storage positions in the first database according to the size sequence of the data codes.
3. A data processing method according to claim 1 or 2, characterized in that the method further comprises:
writing a preset number of feature data into a text file in sequence according to the size sequence of the data codes and the number of the feature data to obtain a plurality of text files of the feature data;
compressing the text files into data packets, and uploading the data packets to a cloud storage server;
when a data updating request of a newly-added terminal is received, determining storage position information of the data packet in the cloud storage server;
and sending the storage position information to the newly added terminal so that the newly added terminal can acquire the data packet from the cloud storage server according to the storage position information.
4. The data processing method according to claim 1, wherein the first database stores a plurality of first feature data corresponding to a first terminal attribution identifier and a plurality of second feature data corresponding to a second terminal attribution identifier, the method further comprising;
if the first terminal attribution identification is different from the second terminal attribution identification, determining a plurality of first characteristic data corresponding to the first terminal attribution identification in the first database, extracting first target characteristic data with a data code larger than the first data code from the plurality of first characteristic data, and sending the first target characteristic data to the first terminal; and (c) a second step of,
if the first terminal attribution identification is different from the second terminal attribution identification, determining a plurality of second characteristic data corresponding to the second terminal attribution identification in the first database, extracting second target characteristic data with a data code larger than the second data code from the plurality of second characteristic data, and sending the second target characteristic data to the second terminal.
5. The data processing method according to any one of claims 1, 2 or 4, wherein the feature data is face feature data for data processing; after the sending the target characteristic data to the first terminal and the second terminal, the method further comprises:
obtaining attendance checking results uploaded by the first terminal and the second terminal respectively; the attendance checking result is generated by comparing the face feature data to be checked with the face feature data stored in the first terminal or the second terminal respectively by the first terminal and the second terminal;
and storing the attendance checking result to a second database corresponding to the first terminal attribution identification.
6. The data processing method of claim 5, wherein prior to the storing the attendance result to a second database corresponding to the first terminal home identity, further comprising:
establishing a second database according to the first terminal attribution identification, wherein the second database is used for storing an attendance result corresponding to the first terminal attribution identification;
establishing a binding relation between the data address of the second database and the home identifier of the first terminal;
and storing the binding relationship in a table form.
7. The data processing method of claim 6, wherein storing the binding relationship in a table form comprises:
taking the attribution identification of the first terminal as a key value, and taking the data address of the second database as a value to form a key value pair;
storing the key-value pair to a key-value database, so that the binding relationship is stored in the key-value database in a table form.
8. A data processing apparatus, comprising:
the receiving module is used for receiving a first request sent by a first terminal and a second request sent by a second terminal; the first terminal and the second terminal are different attendance terminals;
the judging module is used for judging whether a first terminal attribution identification carried by the first request is the same as a second terminal attribution identification carried by the second request;
a first determining module, configured to determine, if the first terminal attribution identifier is the same as the second terminal attribution identifier, a smaller data code as a target data code from a first data code carried by the first request and a second data code carried by the second request;
the first acquisition module is used for acquiring target characteristic data from a first database according to the first terminal attribution identification and the target data code; the first database stores a plurality of characteristic data corresponding to the first terminal attribution identification, each characteristic data corresponds to one data code, and the larger the data code is, the later the time for inputting the corresponding characteristic data into the first database is; the target characteristic data is characteristic data with data codes larger than the target data codes;
and the first sending module is used for sending the target characteristic data to the first terminal and the second terminal.
9. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the method according to any of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1 to 7.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109840962A (en) * | 2018-12-27 | 2019-06-04 | 北京智慧云行科技有限责任公司 | Recognition of face movable attendance checking system and method |
| CN110322578A (en) * | 2019-05-30 | 2019-10-11 | 深圳市融讯科技有限公司 | A kind of Work attendance method and device |
| CN110728760A (en) * | 2018-07-16 | 2020-01-24 | 深圳市鸿合创新信息技术有限责任公司 | Android terminal real-time attendance record statistical presentation method and device |
| CN111681331A (en) * | 2020-06-04 | 2020-09-18 | 安徽鸿程光电有限公司 | Attendance identification system and method |
| CN111696220A (en) * | 2019-03-11 | 2020-09-22 | 杭州海康威视数字技术股份有限公司 | Sign-in method and device |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| FR3009159B1 (en) * | 2013-07-26 | 2017-06-23 | Inst Mines Telecom | METHOD FOR PROCESSING GEOLOCATION DATA |
| US10706731B2 (en) * | 2016-08-10 | 2020-07-07 | Bedford, Freeman & Worth Publishing Group, Llc | Attendance monitoring system |
| CN108776791A (en) * | 2018-06-07 | 2018-11-09 | 中国平安人寿保险股份有限公司 | Client identity identifies method of calibration, device, computer equipment and storage medium |
| CN111079557B (en) * | 2019-11-25 | 2023-08-25 | 山大地纬软件股份有限公司 | Face recognition-based automatic distribution method and system for power business hall customer service terminals |
| CN111475517B (en) * | 2020-03-06 | 2024-02-20 | 平安科技（深圳）有限公司 | Data updating method, device, computer equipment and storage medium |
| CN111860313A (en) * | 2020-07-20 | 2020-10-30 | 长视科技股份有限公司 | Information query method and device based on face recognition, computer equipment and medium |
-
2020
- 2020-12-08 CN CN202011423522.4A patent/CN112669480B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110728760A (en) * | 2018-07-16 | 2020-01-24 | 深圳市鸿合创新信息技术有限责任公司 | Android terminal real-time attendance record statistical presentation method and device |
| CN109840962A (en) * | 2018-12-27 | 2019-06-04 | 北京智慧云行科技有限责任公司 | Recognition of face movable attendance checking system and method |
| CN111696220A (en) * | 2019-03-11 | 2020-09-22 | 杭州海康威视数字技术股份有限公司 | Sign-in method and device |
| CN110322578A (en) * | 2019-05-30 | 2019-10-11 | 深圳市融讯科技有限公司 | A kind of Work attendance method and device |
| CN111681331A (en) * | 2020-06-04 | 2020-09-18 | 安徽鸿程光电有限公司 | Attendance identification system and method |
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